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Feature Selection for Automatic Classification of Gamma-Ray and Background Hadron Events with Different Noise Levels

  • Andrea Burgos-MadrigalEmail author
  • Ariel Esaú Ortiz-EsquivelEmail author
  • Raquel Díaz-HernándezEmail author
  • Leopoldo Altamirano-RoblesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

In this paper we present a feature set for Gamma-ray and Background Hadron events automatic classification. We selected the best parameters combination collected by Cherenkov telescopes in order to make a robust Gamma-ray recognition against different signal noise levels using multiple Machine Learning approaches for pattern recognition. We made a comparison of the robustness to noise for four classifiers reaching an accuracy up to \(90.14\%\) in high noise level cases.

Keywords

Gamma-ray separation Relief-F Machine learning classifiers Robustness to noise 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Instituto Nacional de Astrofísica Óptica y Electrónica (INAOE)PueblaMexico

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